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This study uses Las Vegas near-road measurements of carbon monoxide (CO) and nitrogen oxides (NO x ) to test the consistency of onroad emission constraint methodologies. We derive commonly used CO to NO x  ratios (ΔCO:ΔNO x ) from cross-road gradients and from linear regression using ordinary least squares (OLS) regression and orthogonal regression. The CO to NO x  ratios are used to infer NO x  emission adjustments for a priori emissions estimates from EPA’s MOtor Vehicle Emissions Simulator (MOVES) model assuming unbiased CO. The assumption of unbiased CO emissions may not be appropriate in many circumstances but was implemented in this analysis to illustrate the range of NOx scaling factors that can be inferred based on choice of methods and monitor distance alone. For the nearest road estimates (25m), the cross-road gradient and ordinary least squares (OLS) agree with each other and are not statistically different from the MOVES-based emission estimate while ΔCO:ΔNO x  from orthogonal regression is significantly higher than the emitted ratio from MOVES. Using further downwind measurements (i.e., 115m and 300m) increases OLS and orthogonal regression estimates of ΔCO:ΔNO x  but not cross-road gradient ΔCO:ΔNO x . The inferred NO x  emissions depend on the observation-based method, as well as the distance of the measurements from the roadway and can suggest either that MOVES NO x  emissions are unbiased or that they should be adjusted downward by between 10% and 47%. The sensitivity of observation-based ΔCO:ΔNO x  estimates to the selected monitor location and to the calculation method characterize the inherent uncertainty of these methods that cannot be derived from traditional standard-error based uncertainty metrics.

Variability in Observation-Based Onroad Emission Constraints from a Near-Road Environment